Transcript

Jack Kokko - Building AlphaSense - [Invest Like the Best, EP.404]

0:00 I know firsthand how complex the tech stack is for asset management firms. And seemingly every new tool and data source makes the problem even worse, adding more complexity, more headcount, and more risk. Ridge line offers a better way forward, one unified platform that automates away the complexity across portfolio accounting. Reconciliation, reporting, trading, compliance, and more, all at scale. Ridge line is revolutionizing investment management, helping ambitious firms scale faster.

0:25 Operate smarter and stay ahead of the curve. See what Ridgeline can unlock for your firm. Schedule a demo at ridgeline.ai. Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. Invest Like the Best is part of the Colossus family of podcasts, and you can access all our podcasts, including edited transcripts, show notes, and other resources to keep learning at joincolosis.com.

1:00 Patrick O'Shaughnessy is the CEO of Positive Sun. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of positive some. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc.

1:29 My guest today is Jack Coco. Jack is the CEO and founder of AlphaSense, where I and Positive Sum are investors, an AI-powered search engine for market intelligence. He shares how Alpha Cynthia began by aggregating Fragmented financial data sources. and evolved with the advent of large language models to completely change the research experience for investors and others. He speaks to the recent acquisition of Tegis earlier this year, reshaping the business and further supporting their expansion to serve all types of companies instead of exclusively investment firms.

1:58 Jack has been navigating the AI revolution from its earliest days, and you can feel his excitement when he talks about the future. We discussed building an agile platform, the importance of managing cultural integration, balancing AI capabilities with user trust. and the frontier for this technology. Please enjoy my conversation with Jack Coco.

2:16 So Jack, you are in a very unique position. Having been one of the few entrepreneurs. That was effectively building an AI product. Many years ago. before everyone was talking about AI.

2:28 And using data and search and these tools. Probably for longer than just about anybody. So I think you're uniquely positioned to tell us. what you've learned about applying the technology to build a great product. Today in late twenty twenty four.

2:43 And maybe that's a perfect place to begin, which is just like a state of the union from you. On What kinds of things AI enables for a product builder trying to serve a customer. what it unlocks. I'm also gonna ask about what the limitations are today.

2:59 But since you've been doing it for so long, just give us that felt experience. And then we'll talk about the future. Well, maybe I'll start from the very beginning of what we are building with the prior generations of AI and machine learning. We wanted to build a semantic search engine.

3:14 that would understand financial and business content, read it line by line almost like a human analyst would. and understand all these billions of data points, millions of documents, and connect the dots between them. Then map that to a user's search. query and deliver the right data points, the right insights.

3:34 And this was Really hard to do because All this content was siloed in thousands of silos instead of paywall data sources. It wasn't like the internet where Google and others had billions of web pages with links between them, and those links would tell you about the authority of those pages, and billions of consumers clicking on

3:54 pages that also told you what's good content, what's not so good content, and what's most relevant to a particular query. None of this existed in these fragmented separate databases that we needed to go and access and then semantically index and help users search. And this was a really hard problem. Even Google And

4:12 Others in that space had tried to build enterprise search and had to retreat it from that market because it's so hard to do this without those benefits that you have in the public web. So we use And earlier generations of it. machine learning to categorize information and semantically try to understand the context of words and sentences and then organise that information so we could

4:33 Then map that to each user query. Later on you got the simpler, earlier language models like Google's Bert and Where level of understanding expanded. You were no longer categorizing things or looking at small local context, you started to understand Longer.

4:48 sentences and passages better and really be more accurate. about understanding topics and KPIs and what is a company saying, is it bullish? Is it bearish? Like all of this kept on improving. The categorization, the Linkages between Conversations.

5:04 But the true big breakthrough of course was with large language models and generative models. Where Now you had Massive context, and you could

5:14 Have a model that reads even up to your whole book. Whatever you give it to it. Obviously different models have different capabilities, but the contextual understanding was incredible. And the model now had world knowledge and it was able to make its own conclusions statistically, but still, yeah, apparently to us humans in a pretty smart way. So

5:33 That now enabled you to Do a couple of things that wasn't when possible before you could Take a natural language query from a user. And actually understand it. Take a sentence and really, really solid good

5:46 precise understanding of what the user is looking for. Which now tells the machine a lot more about What the true intent is that you're looking for. In a way that couldn't be expressed in just keywords. And then similarly we had a much greater understanding of all the underlying text because the system understood the context.

6:04 at the really broad level. So it took The capabilities. Another huge leap forward. Enabled even better. mapping off.

6:13 What a user wants. And finding the right contextual insights from these vast volumes. Of information. So

6:21 It's a really exciting space to be in and Like making sense of information has just become so much more Easy and powerful with LLMs now. And of course that's evolving so rapidly that the answer changes. In terms of what's possible almost every month or two.

6:37 Just for context for those listening. Just give us a general sense of the sort of data sets, document sets that Alpha Cents had pre-Chat GPT, pre-LMs, pre-GPT models. What were the key things that your users came to you to find information within those data sets. Yeah, but basically everything that an investment analyst would use in their research process or a corporate strategy or

7:02 competitive intelligence professional or finance professional would use in their research about their competitive landscape and launching new products and things like that. So it was Basic company information, company filings. earnings call and other conference Transcripts.

7:16 Anything a company would publish mandated by the SEC, uh companies put a lot of information out there, so collecting all that. And then collecting everything that the cell side say. So you've got a vast number of banks and their research departments producing a lot of high quality research. But that's in the past been fragmented. So bringing all that together and

7:35 making it available based on who's entitled to what. So kind of figuring all that out and Bringing that to users. Back in those days that was the data set. Mostly information that you might get access to. From

7:49 financial data terminal that you might be using as an investment analyst. Or as a corporate, typically would have less access. to some of these more private proprietary sources like the bank research, but still Those were the types of content. And since then we've obviously added

8:03 A lot more Really insightful proprietary content on top of that. Yeah. And so if I think about oversimplifying, I'm sure, but conceptually the early version of the product is All this relevant data that is in disparate places, disparate silos. and the best possible search tool to like get me the pieces of information I want based on what I'm asking for as quickly as possible.

8:25 Right. It was information that's hiding in plain sight. How do you find the needle in a haystack? Everybody had the haystack, more or less. But it was just a a lot of work. So much work that you often didn't have the time to invest in it. So you had to guess and hope for the best that you had covered enough and

8:40 We're always fearing. that you might be missing something because it was really so inefficient to come through this information. So this allows me to ask a question I'm so interested in, which is okay, so you had been doing this. You had been using machine learning models and I'm sure investing tons of brain power and dollars and people into An amazing fast search experience. Take me back to the day that ChatGPT came out or GPT three came out or whatever the relevant milestone was for you all. How that then changed your world? What changed in the product.

9:11 How did you take in that new capability? Did it displace a lot of the advantage that you had built up? Do you feel as though These general models make it easier for other people to do, let's say, great semantic search in their document database for enterprise search. How did your world change? And how did you react?

9:27 to that first technology and then the subsequent ones that have come out. So again, it was going from these smaller language models with more predictive or Language understanding capabilities, but without generative capabilities. And so with large generative models gave a lot more capability, lot more general purpose. abilities that you could deploy.

9:46 For us that was just an incredible playground and opportunity to go and Just apply these brains in a box as I like to think about them. In lots of different ways. tools that we used to spent the first

10:01 few months. Obvious things to build like summarization of documents and summarization of insights across an industry and so forth, like letting users get access to Cross company insights in a way that would have And

10:18 too tedious in the past and suddenly became very doable and figuring out how to control these LLMs and how to Control build God rails to prevent hallucination and retain some of the capabilities of our search engine where some of the beauty was really in the user experience of being able to really see the individual sentences and snippets of

10:39 text where the insights came from and quickly validate and get to the source context and read the underlying document, really know where all the pieces of information came from. Figuring out how do we deploy this in a way that retains all that greatness win this new world where you're now able to use LMs to do it in a smarter way. Now did that Give. The rest of the market new tools, yes.

11:01 I think that it's in some ways given deceptively interesting tools Well You even see I mentioned earlier. how even Google had an enterprise search product they'd be pulled out of the market because it wasn't

11:12 feasible now you see some large corporations and large financial firms Tinkering around their IT teams. have been given a budget by a CEO to say, Hey, go figure out something to do with Gen AI. And the most obvious thing that seems to always come up is That hey, we've got all this internal content hiding in all kinds of disparate places and

11:34 We've done all this work to produce all these insights and memos and so forth, but When we need it, we don't find it. So can we go solve this? And Suddenly Jen AI gave some tools to those IT teams that allowed them to try to do and create some

11:48 Very interesting demos that then got funding often. And so people started to try to build these things that even Google had backed away from doing in the past. So yes, it it certainly gave Capabilities that weren't there before. But As people started calling this rag retrieval augmented generation

12:05 And started to look at all the components of creating a robust Software system The the reality has still been that okay, you've now Made some new leaps.

12:15 But you also have some new difficulties to sí leaps that this technology enables We feel like We're a company that has spent

12:24 The last decade and some years on top. to really get really good at this. And so we made those leaps quickly. And you've got the large frontier model companies offering their solutions and those are advancing quickly and staying on top of this technology and developing something great that users have now come to expect by using something like a Jet GPT. It's actually now become in some ways even harder because of the really fast pace of development. Just when you think you've built Something great.

12:52 some new great model, advances in its capabilities and That's available to consumers. And now those consumers expect that you have that also in your professional solution. And so I feel like that actually gives us even more advantage as a startup that can move quickly and deploy these capabilities and has a really smart search engine that feeds the Gen AI on top that is doing now the conversation with the user. Yeah.

13:16 this really powerful search engine that semantically kinda finds all the right information so that it can feed it to the Gen AI to summarise. So I feel like this actually has been a massive accelerator for our roadmap and created a ton of even higher demand from the market because we've gotten closer to the original vision we had of a machine you could talk to and get answers from. I always thought it was much further away in terms of when you could actually deliver that. And now we're suddenly

13:43 Able to I could pick up my phone and pick up the offices app and ask a question, any business question. Get the answer. And I thought that was decades away, realistically, but here we are, and that's possible. It's it's a wonderland for a company like us.

13:57 What have you learned so far from user behavior? Everyone can sort of imagine all these cool ideas. And then you put them in front of people and you maybe you never know exactly what they're gonna use. Is there anything that surprised you about what they use or don't use that's newly possible because of these models, whether that be synthesis or summarization or condensing or just better search or what have you learned about the revealed preference of your user? applying this technology.

14:23 Mostly. It's been Fairly Expected. It's just making these Better connections, summarizing across

14:31 Companies, industries having a better natural language conversation with The user instead of keyword based semantics. Maybe what has surprised me is More on what you see on the consumer side where

14:46 People use A chat GPT or perplexity or Gemini, et cetera, and and they in their daily lives In my mind, trust these LMs too much.

14:58 And I find myself doing that too. You don't have infinite time and if you don't have citations, you just have a credible looking L based answer. That you can read and feel true. And let me run with that and check against my own

15:12 recollection if it makes sense. It's too much work. They don't make it easy to go and validate even if that was possible Whatever the album pulls from its vast memory, it doesn't even know where it came from. that consumer world, there's a lot of trust in that. And we see what surprises me a little bit is that we occasionally see and hear about professionals using those same tools and

15:33 defaulting to trusting them in my mind way too much. Most people don't, but that's been a bit of a surprise where we've always worked with serious researchers that do deep dive work and our function is to take them to the right content, but they want to really Read it and get the full context then. See where it came from, what's the analyst that wrote it, and really get to the bottom of something before trusting it, before making seven figure bigger bets.

15:58 based on this information, but maybe the biggest surprise has been that you also do see Professional users. fairly often trusting some of these public LMs too much. If you think about But things that differentiate

16:11 and just the general categories of the tools and the data. Two simple things. You mentioned that you've migrated A lot towards proprietary content that just you have.

16:22 You were an early aggregator of proprietary content cell side research that you could get directly from lots of banks, but not aggregated in one place. How much do you think the separation between the winners and the losers in let's call it enterprise financial search? Will be determined by tooling versus data. Because you see like perplexity now, you can search over financial documents and perplexity, SEC documents or whatever.

16:45 I'm curious how you think the battle shakes out between providers trying to solve this problem for the customer. And how much of it is tools versus how much of it is More and more useful data. That is a

16:56 Great. Question. I think For us. The

17:02 Fundamental problem has always been about fragmentation. I experienced this as As an investment banking analyst where I first came up with the understanding of the problem that needed to be solved in this industry where I was logging into dozens of different systems and asking the library to go to even more places to

17:20 Try to gather information. So The fragmentation was always a huge pain point. Back then it was so fragmented that you were keyword searching a single PDF one keyword at a time. And doing that across lots of PDFs and trying to aggregate this information.

17:34 even with more aggregation, you still find it's a dissatisfying solution unless you have something where you can ask one question from one system. So in that sense It is Both a technology battle.

17:47 Of having a really great Solution. layer on top the smartest L's applied in the smartest way and sequenced in the right way that you can Create.

17:59 Answer, but also And then the user experience that helps users still get comfortable and trust, but verify and validate and get to the context and read. But the underlying content, you want it to be all in one place. You don't want to go and do multiple searches in different systems and learn the intricacies of each one. If you could do Everything in one place. So our strategy from day one has been like hey, we'd love to be one place where one search and done, you can really expect that if you didn't find it in our platform, it doesn't exist. And we often hear clients telling us that's how they think about it, that it gives them the comfort that they didn't have to go to twenty different systems to get that comfort. They could go into one system.

18:37 Yeah, I'm curious also. If you think about Planning A business like yours. for

18:44 technology landscape that just seems to be explosively evolving. How do you do that? If you don't know what GPT six is gonna be like or something like that. How do you do effective

18:55 Planning. Not knowing what the technology itself will allow on the product tooling side. Well you have to try to In my mind, organize your Team and

19:05 The org structure To be agile. Because you just have to expect the unexpected. It's going to happen and it's gonna happen very frequently. So you have to be able to react to new developments and you don't know what they're going to be. Nobody knows really.

19:19 These L and developers, even the frontier model developers, don't know what the next breakthrough might be when you cross some scaling threshold or new algorithmic development. For us the solution has been to First try to create a system that is pretty agnostic that can use any LLM that's out there. and use different systems to accelerate their performance or

19:41 Building. the methods where you can very quickly pivot and change what model you use for what task if there's suddenly some model is better at multimodal or better at a bigger context window as Better at Doing those things. Much faster.

19:56 And so forth. So you can actually you've got less latency, more time to get more intelligence into the system. And so whatever those things are. You want your systems to be more modular so you can orchestrate all this in a much more flexible and agile way. And then have a more agile organization that tracks what's going on and is on top of those new developments. And even when they just come out as research papers You are reading everything that comes from credible sources and

20:21 try to stay on top of what's a real development, what actually could be deployed today, what's not quite there yet. But what's giving us some hints about what might come next. So I find myself s spending a much larger percentage of my time now on just product development and reading everything. Myself, I guess I get more of the filtered information from the team that reads everything and then one out of ten things that really is impactful gets on my screen and

20:46 Many others do the same. Different levels of deep dive and screening. Now for that information, but You've got to be on top of it all the time and willing and able to pivot. I know you can't tell me exactly how

20:59 investors will work in five years that's different than today'cause the future's hard to predict. But I'm sure you can say a little bit about What might be possible for them that isn't possible today. is a sneaky way of asking just what is most exciting to you from a product perspective of what you will enable your customers to do five years from now.

21:19 Because of all this. Put your dreamer hat on. What do you hope you're able to do? for customers five years from now that you can't do today. We're trying to take all the friction and barriers out of the way and try to

21:32 Make the cost of acquiring information and insights really, really low. So you could suddenly scale it and do a lot more work. To a lot more diligence. And have it be organized for you and done for you by machines. So that the things that used to take weeks that you don't have the intern around to say, Go research this for three weeks, but if it takes three minutes because the machine can organize that information and ask the same

21:57 five questions from five hundred documents and say this is the most important set of documents really you should read and Maybe some tentative conclusions that you could make. hard to really see how soon and how far that goes, but I like to think of it as the

22:13 computer systems and uh Large language models within them. Doing a lot of the work and acting as A large Team of superhuman analysts.

22:24 That gets smarter and smarter and Maybe there is a large group of In turns that now turns through a lot of the work that would have in the past required a long time and didn't get done. Now it can get done. So the level of diligence, level of research. And then the systems get smarter and they can start to draw some tentative conclusions and you can start to evaluate out of these five that the machine generated.

22:46 These are the five scenarios where now you can start to play with them as an investor. Yeah, it's so exciting to imagine that you might be able to say, Build me a industry primer on XYZ and have it be done in Two to three minutes. That's pretty exciting. Right. Those kinds of things are very much. Doable today. But then what do you do with that? And maybe you let the machine take

23:05 alternative views at the industry and try to simulate where could that go? And It's not gonna be able to tell you where it will go, but it can tell you different paths of where it might go. What data sets are most exciting to you? One of the things about the Alpha Cent story that's clear, we were a big investor in T Gis and obviously you acquired Tegus. That's an incredibly valuable data set. I have questions about

23:24 M and A and the role it plays in building businesses in a little bit. First How do you know you're onto a data set? That is. interesting, different, useful

23:35 I'm sure you've looked at every data set imaginable. What are the features of data sets that make them valuable? To me it's the net addition to the information volume that's out there already in terms of

23:48 Impact Of Those insights. Are they overlapping with what's already out there or are they totally new perspectives on something? And you rarely get that new perspective.

23:59 The expert interview content is definitely what I see as the most exciting one because it provides a whole new lens into What's going on with companies and industries and private markets where Those markets have

24:12 been lacking transparency. I've been in the dark in terms of information you could get on those companies. In the public markets. Which are becoming a smaller and smaller part of the total picture. You've got SEC telling companies exactly what information they need to be putting out at what frequency.

24:29 And you've got the sell side writing research on those companies and You've got Pretty good information out there on public companies, but even that is filter. The companies try to do their best to show their best sides of their business and get their best story out, positive story. So interviewing those experts, getting buy side interviews with investment analysts that are about to invest in a company, interviewing an expert. at a company, former executive, customer, partner, competitor

24:55 And really asking them the most important questions about the business model, the competitive environment. the company demand environment and so forth and getting real answers from real people that don't have any skin in a game to

25:07 Alter that viewpoint. That is incredibly valuable. And even adds ton of value to public companies. impartial.

25:16 additional set of perspectives from people in the trenches. The operators. But then y now you look at the same new capability for A new source of insight for private companies where

25:26 There's almost nothing. out there because there is no regulatory need to put out information. So this now fills a much larger vacuum. The private markets are becoming so much more important. But there still isn't anything out there that you can systematically get to other than expert interviews. And so that's what got me so excited about that space. And I'm still thinking this is the most exciting area of kind of new content and new insight about companies and industries.

25:53 out there. So we're really doubling down on this in our business. With the Tigus acquisition relatively recent, talk about the role that M and A can play in building a business towards a specific vision. And just the lessons you've learned doing M and A.

26:08 Not that many people get to do lots of large scale M and A. What have you learned about doing it well? What mistakes have you made? They've taught you lessons. I'm just curious about this part of the business story. It's Actually funny in that I started my career

26:22 working on M and A as an analyst in investment banking and I thought I knew a thing or two about it, but when we were looking at our first acquisition a few years ago I was still wondering, Okay, what is this going to be about? What do I need to know? I couldn't find any rule book or instruction manual to it.

26:38 It's shocking how little there is really out there. Some podcasts, some of your guests have been really smart, Brad Jacobs, and you've got people that have done a lot of M and A and you try to read it, but you have to collect it here and there. In an MBA program you don't learn this. How do you go and acquire a company and then integrate it and really leverage it in your business. So you have to learn it yourself. So it was little.

26:57 Daunting at first, but still proceeding Tigers, it was kind of taking us a step in that same direction of an expert interview library. Despite those concerns, the strategic logic was so incredible that it felt like a drop everything kind of thing. So it's a okay, we're gonna have to go and just Do this, learn this and Figure out how to do it.

27:15 And then you feel like okay, we got this one done and it actually went really well and the strategic logic made all the sense in the world and investors are really excited about the outcome and okay, this actually Yes, you have landmines along the way, but if you're careful, maybe you can avoid them even without having Gotten the course on this. So yeah, then we did a couple of more and then got the really fortunate incredible opportunity with Tigers earlier this

27:40 year where we had been the number two in that space, but a distant number two, where Tigas had really innovated and created a this incredible business model. And created sort of great scale. That we were far behind. That was the bigger bite and figuring out With our board, now investors could we get something like this done?

27:59 getting close to a billion for a startup. company worth a few billion itself. Can we really Right. A lot of cash very quickly.

28:09 And a process that requires you to move quickly. One learning was that it was worth patiently building an investor base that has deep pockets where you didn't need the capital, but When you know it was there and suddenly everybody was able to pitch in and say I was shocked excited about how quickly people just were willing to go back it because they said, Yeah, this makes perfect sense. If you have strategic logic and two products that really belong together and are one plus one is going to be four or five. Then

28:38 Yeah, absolutely. People are gonna very quickly without doing much extra research, they knew both products and said, Yeah, these two belong together. And so that was a big learning as well, that even something much, much bigger Investors. By integrate strategic logic. Certainly not into doing acquisitions for the sake of acquisitions. I would never go for that either. Just try to buy

28:58 scale but the strategic logic of building something really valuable that was Super exciting and I was excited to get the investors behind it so quickly as well. Felt like you could suddenly Playing

29:10 a bigger league when you have the capital behind you. Knowing that that class Brad Jacobs and a few others have left breadcrumbs all over the place for how to do this well, but knowing the class doesn't exist, having now done it several times and how to be a creative. What would be your

29:25 syllabus for a class that you would teach entrepreneurs that want to do MA. How would you guide them on here's how to think about this. Beyond obviously make sure that they your buying has the strategic alignment and there's some strategic insight. This helps you accomplish your mission faster or better.

29:40 What other components of that playbook would you write if you had to write it? The thing that I found the least on is Of course, you've got some textbooks on deal negotiations and structuring and all that stuff, but I don't think that's where the hard PCs are. It's more

29:56 Okay, once you put two organizations together, how do you figure out how the cultures are compatible or what work you need to do to Make sure that people quickly trust each other and Always when You try to imagine yourself on the other side being acquired. What do they need to hear day one to feel Good that hey this

30:14 New company that has just Uh everything changed. Can we feel confident in what they're planning to do here and their intentions and really actually intent on growing this and

30:27 doubling down what we're doing and how do you get them really excited about the future when suddenly they're fearing all the natural fears that come with an acquisition. So those softer factors and identify all the great talent and what they're capable of. There isn't any system that I'm aware of for that. It's still kind of very manual work. So far w works.

30:49 Is it overcommunication early on? What is it? What are the elements that you did do that did work in that direction. think it's really important to try to anticipate like what's in the minds of people on the other side. than trying to address their concerns very quickly.

31:06 I'm developing my own flag book here. After a few acquisitions, trying to show up there day one with Answers to the questions that people Really care about. Trying to be

31:18 honest that we don't know everything, but we're really excited about your business and we're really excited about your team and all your talent here and We are. A lot bigger and today we're gonna be even bigger and together we're gonna be even bigger and we're gonna grow faster. And that's gonna create so many career opportunities for everybody. We can't tell you exactly what they're gonna be for all of you, but they're gonna be great because we are a high growth company and here's what's exciting about that. So Trying to think about how do you

31:42 Calm people's Fears that When they read about MA. people naturally have in their heads, so try to anticipate those and address those. But also then try to figure out how do you make this new story that suddenly appeared for them.

31:54 Ex an exciting story where they think that hey, if if I ride this wave This can be pretty awesome. Maybe there's a public company one day where I was able to make a big impact and this is the story I tell my grandchildren. What have you learned about selling a tool to investors. Investor

32:10 specifically as a buyer, I'm curious about corporates too. Maybe it's the same question. What is the key to selling something to them? As a buying group. It's definitely an interesting buying group. This was where we started.

32:25 figured out the need from the perspective of an investment banker from my kind of own experience. figured that it was really g gonna be hard to sell these to these big banks. They were conservative. You are who do you how do you even figure out who can buy for whom And these big departments, but small hedge funds we figured out were actually really Good.

32:45 Customers if you can add value. people have the ability to spend money because they are Working really hard. And there's this intense pressure to find some advantage and edge and If you can give it to them and if you can prove that edge.

33:00 They'll be happy to pay for it. you need to continue to prove that you have that edge and advantage for them. But if you do, then now you've got some Great. adoption in a market pretty quickly. What surprised me about them

33:12 though was they often in especially in the early days and years they often wanted to keep it as their own secret weapon. We thought that hey now the word will spread and everybody will know but a high word of mouth group. No, no, exactly. And there might be some psychology around it as well. Like if you're going to be an investment analyst, you might not be tweeting things as much, although that happens. Fair month today, but felt like they weren't helping us a lot in marketing it, just like a corporate buyer would. But still a great, really agile, aggressive, ambitious small businesses. If you think of hedge funds that way, then they were a great customer base to start with. And then

33:49 From there you could take the Jump to corporations that had more established buying processes. Yeah, what is the biggest difference in selling to corporations versus investors? Just the normal procurement process is slower or the value propos I'm curious if the value proposition framing had to be different. It was harder to find the people.

34:08 When you're selling in a large corporation, often really hard to figure out what's the title of a person that is doing research that were Trying to Make or advocate for some million dollar Or a billion dollar decision and acquisition and product launch, et cetera. Like

34:25 The variety of titles is so broad that it was hard to find those people. You often had to get some shirt pot inside of a company to show you around and Tell you who would be the your next buyers. Because they were so large, they were harder to navigate. So it's taken many years and we still continue to find new

34:43 Personas and We started from investigations and investigations took us to corporate strategy and financial planning and competitive intelligence and then corporate development, you know, acquisitions. Then Strategic marketing and product development, product marketing. Sales even, sales engineering.

35:01 There were just these. new roles and each one of those had many titles depending on the company you were selling to and you had to find these nooks and crannies. globally inside those organizations to find the people. So that was Definitely very different from a hedge fund where you can walk into a floor and find most people there.

35:18 Why do you care about this business? Like what is it in your own experience The customer The cocktail of things that go into a business. What is the why for you beneath Obviously you have a custom, you do something for them, that's the mission, but if I asked you why five times

35:35 You personally? Why did you start this thing? Why do you wanna continue to run it? Why are you Ambitious to make it much larger than it is. This is a great question for any entrepreneur. Why are you gonna keep on doing this for a long period of time? For me

35:49 The Y started to stare at me. In my first startup that I was doing during my studying electronical engineering at finance and I tech and finance together and

35:59 Go work. at a start up stock exchange in Brussels in During my studies called E stack. It was trying to build the European Nasdaq. I had this affinity to the idea that hey you could build a startup and you could take it public and really grow it fast and find all those resources through public market investors. I'd seen my

36:19 dad building a biotech company in Finland where I grew up and it was really hard. It was very hard to find investors and Get funding. In a way that in the US you've got much more developed capital markets. So ESTAC was trying to build that in Europe and say here's a path for European companies to go public and I jumped in there and was helping to raise money working with the CEO to

36:38 call all these big investment banks and see, would you invest in this? And we'll create this new ecosystem. And I was just fascinated by All this. companies that go public and they become more professional on the all these disclosures that they put out and it was like happy, excited reading prospectus documents. I think very few people, certainly very few tech people would be excited about that. So maybe there was something about that and

37:01 The other thing I was really excited about was Building a startup and so It wasn't really the right time for Europe didn't have enough exciting tech companies to Sustain that. European Nasdaq, so

37:12 I ended up going into investment banking and then finding my way to Silicon Valley. working with tech companies and investment banking and Just continue to be super excited about How those companies now in this like

37:23 Most powerful tech ecosystem in a world where starting from humble beginnings and very quickly zooming out to be very important businesses and how finance was able to help them and how you had this developed capital market system that

37:37 Where information was out there and was pre produced and researched and So I felt like hey there's something here that I could do to really add value. I No how

37:47 Really in my bones as an analyst, I still I remember walking into client board meetings and Fearing how the information I was able to acquire in the two days that I had time to research their business, I'm just gonna be exposed. the CEO of that company or the boy is gonna see this analyst is just doesn't know what they need to know. And that was because the process was so manual and

38:08 inefficient of acquiring information. So For me, it's still the source for inspiration because I know there are millions of people out there in finance and business that are still struggling with the inefficiency of it, then I feel like If I can pull my passion here and our technology and our

38:25 capabilities together in the best possible way to help the whole business and investing world to be more efficient in acquiring information, making better decisions, deploying capital better. Then I've made a contribution. Sort of like Steve Jobs talks about wanting to put a dent in a universe. You wanna find something that you can uniquely do that nobody else was trying to do and somehow it was being ignored and

38:46 Financial disclosures. Gives me an edge. Perhaps. There was a chart floating around last couple of days showing

38:56 since I can't remember what year The technology businesses and the market caps shown by the size of the bubble in the US and then in Europe. It's what you would imagine it looks like. It's this massive dominance by the US. Versus Europe.

39:10 Lot of people have written about this. What do you think is the reason why this is the case? What are the missing conditions? in Europe, which has a history of many incredible businesses. at least in the last twenty, twenty five years, been unable to produce

39:24 the sorts of technology businesses that have thrived in the US. So firstly I would say No. Europe is producing some. Certainly

39:33 Not Anywhere near the rate of the US? And often they get acquired by US companies, so they don't fly as far and high. It's just really hard. to get going from a European entrepreneur's perspective where

39:47 It's harder to raise money. If you want to find capital to scale, you probably have to go to the US. At least you have to go to London, but you probably have to go to the US. Yeah, have a harder time finding experience. Founders co founders.

40:00 And employees. There's a bit of a different culture and American culture is More friendly to working around the clock and There is always the same one in Europe. You've got more risk aversion. It's sometimes

40:12 Just hard to find people that are willing to join an early stage startup. They'd rather work for some Let's say developers working for a software consultancy which

40:23 fear the the risk in startups much more. Where in the US people admire and get excited about building something new and are fine with the risk and entrepreneurs are more fine with the risk. Failure is Considered just a step in the way to success.

40:38 Whereas in Europe it might be Terminal state. Yeah, right. It's a big chicken and egg problem and the flywheels has been getting going and it's producing some successes, but It's hard when it's a chicken and egg problem.

40:49 having the entrepreneurs, the ecosystem, the investors and everything feeling itself. It takes a long time to build. And so our attempt with E stack uh Way, way too early. I don't know if that might succeed better today, but still it might be early. If you think on the entire history of the business so far. What do you think the largest mistake that you've made is?

41:10 And looking back on whatever that is. Do you take any specific lesson from it? I'm just always fascinated to learn from people's mistakes. The thing as an entrepreneur, as a s CEO of scaling a company, you always think that I've done something wrong. I should have scaled faster.

41:28 feel so obvious that of course this is successful like no Everybody look at this thing that you really killed it ten years ago. Yeah, see, if you had believed in it more, if you had invested more earlier, then then I look at myself, why didn't I convince them of this bigger story earlier? So maybe that's the one mistake of failure where they wasn't confident enough to like tell them that this will be amazingly successful.

41:50 Don't ask me for more milestones. Let's just go. If you think about I ask you to really zoom in to a moment. What do you think the defining moment So far of AlphaSense.

42:01 has been. Maybe the one moment is uh breaking into the corporate market. there was always question of if you're only selling to financial services, then how big is that addressable market? And Nobody had really done this before building a financial services financial research platform and Taking it to the corporate market.

42:21 Everybody talked about it and said, Well, wouldn't that be great? And here's our corporate story. Everybody had that story, but it Nobody had really proven it. And so when we managed to prove that actually we had a search engine for Професіонал, бізнес професіоналс, на час фінансово професіоналс. that breakthrough and proving that addressable market was ten times bigger.

42:40 Covering really every type of business. I think that was the most defining moment where we could say, Okay, we're no longer a niche business. We're actually a horizontal solution that has massive Opportunity for scale. Can you tell me that story? How did you do it? What were the key ingredients?

42:57 We had the assumption that somehow we'd have to change the product, but we'd just try to sell it first to corporate users that where we were just coming into contact with and invest relations was already a function that was dealing with investors and and we learned that okay, they needed to be smart about what the investors were looking at and researching when they were evaluating whether to buy their stock or not.

43:19 And they wanted to get up to speed on that, get smarter about that and Even try to replicate the analysis that the device side was doing and Tell a better story, smarter story, understand the competitive landscape in the way that an investor would look at it. We found that we could do we expand quickly, we could get them to talk to their peers across firms and

43:41 The word of mouth really spread quickly and Suddenly we've started to get large numbers of investor relations departments to jump on board. And we doubled down on that and said let's hire more people to sell into corporates and really try to make make it big and it worked. A good beachhead. Yeah. Yeah, exactly. And then saying, Okay, this works. We didn't have to change the product. Let's just

44:01 Go. Is there any peer? person, founder, CEO, or company. That You watch most closely or learn the most from?

44:11 I watch the foundational model developers with fascination. Just how quickly they've been able to Get adopted and I was having a hard time believing that in a business that seemed to be commoditizing with LMs being open sourced and

44:30 For me. user perspective, you can suddenly get this Incredible product that couple of months ago was hundred times more expensive somebody really Got it to be in the market for pennies. And still

44:41 They just keep on. really aggressively growing and finding ways to Scale faster than anybody has ever scaled. So I watched that with fashionation for sure. I think a lot of people probably do. Is there any part of the models themselves that you're most excited about getting better?

44:57 Certainly reasoning capabilities are valuable. Right now we're building that in our own system. Building the planning to ask a research question from the system, like you would ask an analyst and Let's say it's like what is the market landscape for

45:12 semiglutides and the system breaks it down and says, Well That means I need to understand the market size and the players and growth rates, et cetera. You know, got all those kinds of things. But Maybe the LLMs can get really smart at reasoning and do this type of work natively.

45:30 That would be exciting or being able to really expand the context window while still Keeping Speed. high and cost low and being able to do

45:41 Multi modal. Analysis. video, audio just process things really fast and accurately. Which already is possible, but you gotta do everything in a way that is sufficiently

45:52 Accurate that you natively rely on the model as opposed to plugging together multiple layers of verification and validation checking. Right now you have to build a lot of guardrails. around LMs and have a sequence of those models where one is interpreting what work needs to be done and planning the work and then

46:11 doing their work and then another one is checking their work and Another one is really cleaning up for a user and so there's a big sequence. And If you had a really smart model, you could maybe do many of those things. All at once. Of course, that's what people are trying to build with They call AGI.

46:26 A smarter system that can just think for itself. But right now you plug these things together and create a sequence and create a system orchestrate. Can you can do those things pretty well, but more intelligence is better. As a user of Tigas and AlphaSense. And an investor My conclusion from so much interaction with these tools is

46:44 If you're an investor or an analyst You better not hang your hat on the ability to just go find information and synthesise it. These tools are making it Better. That's a good thing in the net because it just makes the market more efficient, probably. And allocates capital better. The exact thing you said your underlying why is. So I think it's so cool. what you're building and how you're building it and I appreciate you doing this with me.

47:04 Every time I do an interview like this, I ask the same traditional closing question. What is the kindest thing that anyone's ever done for you? First thing that comes to mind is A nerdy teenager really excited about electronics and growing up in a middle class family in northern Finland and

47:21 Somehow my parents allowed me to just go wild in diving into electronics and ordering components from around the world and I felt that that was Kind of them to let me this pursue my nerdy passions. And I felt like I got a lot out of that. And so it feels like they thought that maybe that dirty passion was worth backing. So that comes to mind. Jack, thank you so much for your time.

47:44 Thank you. Um If you enjoyed this episode, check out Join Colossus.com. There you'll find every episode of this podcast complete with transcripts, show notes, and resources to keep learning. You can also sign up for our newsletter, Colossus Weekly, where we condense episodes to the big ideas, quotations, and more, as well as share the best content we find on the internet every week.